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Parametric models are used to estimate the lifetime benefit of an intervention beyond the range of trial follow-up. Recent recommendations have suggested more flexible survival approaches and the use of external data when extrapolating. Both of these can be realized by using flexible parametric relative survival modeling. The overall aim of this article is to introduce and contrast various approaches for applying constraints on the long-term disease-related (excess) mortality including cure models and evaluate the consequent implications for extrapolation. We describe flexible parametric relative survival modeling approaches. We then introduce various options for constraining the long-term excess mortality and compare the performance of each method in simulated data. These methods include fitting a standard flexible parametric relative survival model, enforcing statistical cure, and forcing the long-term excess mortality to converge to a constant. We simulate various scenarios, including where statistical cure is reasonable and where the long-term excess mortality persists. The compared approaches showed similar survival fits within the follow-up period. However, when extrapolating the all-cause survival beyond trial follow-up, there is variation depending on the assumption made about the long-term excess mortality. Altering the time point from which the excess mortality is constrained enables further flexibility. The various constraints can lead to applying explicit assumptions when extrapolating, which could lead to more plausible survival extrapolations. The inclusion of general population mortality directly into the model-building process, which is possible for all considered approaches, should be adopted more widely in survival extrapolation in health technology assessment. • Extrapolation of all-cause survival from randomized controlled trial data is often required in the context of economic evaluation of novel interventions. External information can be used to guide long-term survival trends when extrapolating all-cause survival. This article investigates relative survival modeling approaches to incorporate general population mortality rates into long-term extrapolation. In doing so, we expand on existing approaches by clearly detailing how to apply various constraints on the long-term disease-specific mortality in this modeling framework. • Flexible parametric survival models can apply various extrapolation approaches in the framework. We compare models by imposing constraints on parameter estimation and applying those constraints at specific points of follow-up, including beyond the range of the data. We compare these methods in scenarios where cure is simulated to be reasonable and in cases where it is not. The result shows that different assumptions on the excess hazards can result in different extrapolations of all-cause survival. The various constraints can lead to more plausible survival extrapolation depending on the disease or treatment characteristics. • The approaches for all-cause survival extrapolation outlined offer a suite of possibilities to clearly describe an approach to extrapolation that automatically incorporates general population mortality rates while making explicit choices on how long disease-related mortality will affect the cohort. The choice of approach can be dictated by the clinical context in the decision-making process. [ABSTRACT FROM AUTHOR]

Background: Completeness of recording for cancer stage at diagnosis is often historically poor in cancer registries, making it challenging to provide long-term stage-specific survival estimates. Stage-specific survival differences are driven by differences in short-term prognosis, meaning estimated survival metrics using period analysis are unlikely to be sensitive to imputed historical stage data.Methods: We used data from the Surveillance, Epidemiology, and End Results (SEER) Program for lung, colon and breast cancer. To represent missing data patterns in less complete registry data, we artificially inflated the proportion of missing stage information conditional on stage at diagnosis and calendar year of diagnosis. Period analysis was applied and missing stage at diagnosis information was imputed under four different conditions to emulate extreme imputed stage distributions.Results: We fit a flexible parametric model for each cancer stage on the excess hazard scale and the differences in stage-specific marginal relative survival were assessed. Estimates were also obtained from non-parametric approaches for validation. There was little difference between the 10-year stage-specific marginal relative survival estimates, regardless of the assumed historical stage distribution.Conclusions: When conducting a period analysis, multiple imputation can be used to obtain stage-specific long-term estimates of relative survival, even when the historical stage information is largely incomplete. [ABSTRACT FROM AUTHOR]

The Clinical Practice Research Datalink (CPRD) is a widely used data resource, representative in demographic profile, with accurate death recordings but it is unclear if mortality rates within CPRD GOLD are similar to rates in the general population. Rates may additionally be affected by selection bias caused by the requirement that a cohort have a minimum lookback window, i.e. observation time prior to start of at-risk follow-up. Standardised Mortality Ratios (SMRs) were calculated incorporating published population reference rates from the Office for National Statistics (ONS), using Poisson regression with rates in CPRD GOLD contrasted to ONS rates, stratified by age, calendar year and sex. An overall SMR was estimated along with SMRs presented for cohorts with different lookback windows (1, 2, 5, 10 years). SMRs were stratified by calendar year, length of follow-up and age group. Mortality rates in a random sample of 1 million CPRD GOLD patients were slightly lower than the national population [SMR = 0.980 95% confidence interval (CI) (0.973, 0.987)]. Cohorts with observational lookback had SMRs below one [1 year of lookback; SMR = 0.905 (0.898, 0.912), 2 years; SMR = 0.881 (0.874, 0.888), 5 years; SMR = 0.849 (0.841, 0.857), 10 years; SMR = 0.837 (0.827, 0.847)]. Mortality rates in the first two years after patient entry into CPRD were higher than the general population, while SMRs dropped below one thereafter. Mortality rates in CPRD, using simple entry requirements, are similar to rates seen in the English population. The requirement of at least a single year of lookback results in lower mortality rates compared to national estimates. [ABSTRACT FROM AUTHOR]

Background: A lack of available data and statistical code being published alongside journal articles provides a significant barrier to open scientific discourse, and reproducibility of research. Information governance restrictions inhibit the active dissemination of individual level data to accompany published manuscripts. Realistic, high-fidelity time-to-event synthetic data can aid in the acceleration of methodological developments in survival analysis and beyond by enabling researchers to access and test published methods using data similar to that which they were developed on.Methods: We present methods to accurately emulate the covariate patterns and survival times found in real-world datasets using synthetic data techniques, without compromising patient privacy. We model the joint covariate distribution of the original data using covariate specific sequential conditional regression models, then fit a complex flexible parametric survival model from which to generate survival times conditional on individual covariate patterns. We recreate the administrative censoring mechanism using the last observed follow-up date information from the initial dataset. Metrics for evaluating the accuracy of the synthetic data, and the non-identifiability of individuals from the original dataset, are presented.Results: We successfully create a synthetic version of an example colon cancer dataset consisting of 9064 patients which aims to show good similarity to both covariate distributions and survival times from the original data, without containing any exact information from the original data, therefore allowing them to be published openly alongside research.Conclusions: We evaluate the effectiveness of the methods for constructing synthetic data, as well as providing evidence that there is minimal risk that a given patient from the original data could be identified from their individual unique patient information. Synthetic datasets using this methodology could be made available alongside published research without breaching data privacy protocols, and allow for data and code to be made available alongside methodological or applied manuscripts to greatly improve the transparency and accessibility of medical research. [ABSTRACT FROM AUTHOR]

Background: When quantifying the probability of survival in cancer patients using cancer registration data, it is common to estimate marginal relative survival, which under assumptions can be interpreted as marginal net survival. Net survival is a hypothetical construct giving the probability of being alive if it was only possible to die of the cancer under study, enabling comparisons between populations with differential mortality rates due to causes other the cancer under study. Marginal relative survival can be estimated non-parametrically (Pohar Perme estimator) or in a modeling framework. In a modeling framework, even when just interested in marginal relative survival it is necessary to model covariates that affect the expected mortality rates (e.g. age, sex and calendar year). The marginal relative survival function is then obtained through regression standardization. Given that these covariates will generally have non-proportional effects, the model can become complex before other exposure variables are even considered.Methods: We propose a flexible parametric model incorporating restricted cubic splines that directly estimates marginal relative survival and thus removes the need to model covariates that affect the expected mortality rates. In order to do this the likelihood needs to incorporate the marginal expected mortality rates at each event time taking account of informative censoring. In addition time-dependent weights are incorporated into the likelihood. An approximation is proposed through splitting the time scale into intervals, which enables the marginal relative survival model to be fitted using standard software. Additional weights can be incorporated when standardizing to an external reference population.Results: The methods are illustrated using national cancer registry data. In addition, a simulation study is performed to compare different estimators; a non-parametric approach, regression-standardization and the new marginal relative model. The simulations study shows the new approach is unbiased and has good relative precision compared to the non-parametric estimator.Conclusion: The approach enables estimation of standardized marginal relative survival without the need to model covariates that affect expected mortality rates and thus reduces the chance of model misspecification. [ABSTRACT FROM AUTHOR]

As cancer patient survival improves, late effects from treatment are becoming the next clinical challenge. Chemotherapy and radiotherapy, for example, potentially increase the risk of both morbidity and mortality from second malignancies and cardiovascular disease. To provide clinically relevant population‐level measures of late effects, it is of importance to (1) simultaneously estimate the risks of both morbidity and mortality, (2) partition these risks into the component expected in the absence of cancer and the component due to the cancer and its treatment, and (3) incorporate the multiple time scales of attained age, calendar time, and time since diagnosis. Multistate models provide a framework for simultaneously studying morbidity and mortality, but do not solve the problem of partitioning the risks. However, this partitioning can be achieved by applying a relative survival framework, allowing us to directly quantify the excess risk. This article proposes a combination of these two frameworks, providing one approach to address (1) to (3). Using recently developed methods in multistate modeling, we incorporate estimation of excess hazards into a multistate model. Both intermediate and absorbing state risks can be partitioned and different transitions are allowed to have different and/or multiple time scales. We illustrate our approach using data on Hodgkin lymphoma patients and excess risk of diseases of the circulatory system, and provide user‐friendly Stata software with accompanying example code. [ABSTRACT FROM AUTHOR]

Background: This paper investigates the difference in survival of melanoma patients across stage and sex by utilising net survival measures. Metrics are presented at both the individual and population level.Methods: Flexible parametric models were fitted to estimate life-expectancy metrics to be applied to a group of 104,938 subjects with a melanoma skin cancer diagnosis from 2000 to 2017. Period analysis was used for better predictions for newly diagnosed patients, and missing-stage information was imputed for 9918 patients. Female relative survival was assigned to male subjects to demonstrate the survival discrepancies experienced between sexes.Results: At the age of 60, males diagnosed at the regional stage lose an average of 4.99 years of life compared to the general population, and females lose 4.79 years, demonstrating the sex variation in expected mortality. In 2017, males contributed 3545 more life years lost than females, and a potential 1931 life years could be preserved if sex differences in survival were eliminated.Conclusions: This study demonstrates the survival differences across population subgroups as a result of a melanoma cancer diagnosis. Females experience better prognosis across age and stage at diagnosis; however, further investigation is necessary to better understand the mechanisms behind this difference. [ABSTRACT FROM AUTHOR]

Background: In population-based cancer survival studies, the most common measure to compare population groups is age-standardized marginal relative survival, which under assumptions can be interpreted as marginal net survival; the probability of surviving if it was not possible to die of causes other than the cancer under study (if the age distribution was that of a common reference population). The hypothetical nature of this definition has led to confusion and incorrect interpretation. For any measure to be fair in terms of comparing cancer survival, then differences between population groups should depend only on differences in excess mortality rates due to the cancer and not differences in other-cause mortality rates or differences in the age distribution.Methods: We propose using crude probabilities of death and all-cause survival which incorporate reference expected mortality rates. This makes it possible to obtain marginal crude probabilities and all-cause probability of death that only differ between population groups due to excess mortality rate differences. Choices have to be made regarding what reference mortality rates to use and what age distribution to standardize to.Results: We illustrate the method and some potential choices using data from England for men diagnosed with melanoma. Various marginal measures are presented and compared.Conclusions: The new measures help enhance understanding of cancer survival and are a complement to the more commonly used measures. [ABSTRACT FROM AUTHOR]

Background: Cancer survival statistics are typically reported by using measures discounting the impact of other-cause mortality, such as net survival. This is a hypothetical measure and is interpreted as excluding the possibility of cancer patients dying from other causes. Crude probability of death partitions the all-cause probability of death into deaths from cancer and other causes.Methods: The National Cancer Registration and Analysis Service is the single cancer registry for England. In 2006-2015, 1,590,477 malignant tumours were diagnosed for breast, colorectal, lung, melanoma and prostate cancer in adults. We used a relative survival framework, with a period approach, providing estimates for up to 10-year survival. Mortality was partitioned into deaths due to cancer or other causes. Unconditional and conditional (on surviving 1-years and 5-years) crude probability of death were estimated for the five cancers.Results: Elderly patients who survived for a longer period before dying were more likely to die from other causes of death (except for lung cancer). For younger patients, deaths were almost entirely due to the cancer.Conclusion: There are different measures of survival, each with their own strengths and limitations. Careful choices of survival measures are needed for specific scenarios to maximise the understanding of the data. [ABSTRACT FROM AUTHOR]

Multistate models are increasingly being used to model complex disease profiles. By modelling transitions between disease states, accounting for competing events at each transition, we can gain a much richer understanding of patient trajectories and how risk factors impact over the entire disease pathway. In this article, we concentrate on parametric multistate models, both Markov and semi-Markov, and develop a flexible framework where each transition can be specified by a variety of parametric models including exponential, Weibull, Gompertz, Royston-Parmar proportional hazards models or log-logistic, log-normal, generalised gamma accelerated failure time models, possibly sharing parameters across transitions. We also extend the framework to allow time-dependent effects. We then use an efficient and generalisable simulation method to calculate transition probabilities from any fitted multistate model, and show how it facilitates the simple calculation of clinically useful measures, such as expected length of stay in each state, and differences and ratios of proportion within each state as a function of time, for specific covariate patterns. We illustrate our methods using a dataset of patients with primary breast cancer. User-friendly Stata software is provided. [ABSTRACT FROM AUTHOR]

Background: Life expectancy is a simple measure of assessing health differences between two or more populations but current life expectancy calculations are not reliable for small populations. A potential solution to this is to borrow strength from larger populations from the same source, but this has not formally been investigated. Methods: Using data on 451,222 individuals from the Clinical Practice Research Datalink on the presence/absence of intellectual disability and type 2 diabetes mellitus, we compared stratified and combined flexible parametric models, and Chiang's methods, for calculating life expectancy. Confidence intervals were calculated using the Delta method, Chiang's adjusted life table approach and bootstrapping. Results: The flexible parametric models allowed calculation of life expectancy by exact age and beyond traditional life expectancy age thresholds. The combined model that fit age interaction effects as a spline term provided less bias and greater statistical precision for small covariate subgroups by borrowing strength from the larger subgroups. However, careful consideration of the distribution of events in the smallest group was needed. Conclusions: Life expectancy is a simple measure to compare health differences between populations. The use of combined flexible parametric methods to calculate life expectancy in small samples has shown promising results by allowing life expectancy to be modelled by exact age, greater statistical precision, less bias and prediction of different covariate patterns without stratification. We recommend further investigation of their application for both policymakers and researchers. [ABSTRACT FROM AUTHOR]

Competing risks arise with time-to-event data when individuals are at risk of more than one type of event and the occurrence of one event precludes the occurrence of all other events. A useful measure with competing risks is the cause-specific cumulative incidence function (CIF), which gives the probability of experiencing a particular event as a function of follow-up time, accounting for the fact that some individuals may have a competing event. When modelling the cause-specific CIF, the most common model is a semi-parametric proportional subhazards model. In this paper, we propose the use of flexible parametric survival models to directly model the cause-specific CIF where the effect of follow-up time is modelled using restricted cubic splines. The models provide smooth estimates of the cause-specific CIF with the important advantage that the approach is easily extended to model time-dependent effects. The models can be fitted using standard survival analysis tools by a combination of data expansion and introducing time-dependent weights. Various link functions are available that allow modelling on different scales and have proportional subhazards, proportional odds and relative absolute risks as particular cases. We conduct a simulation study to evaluate how well the spline functions approximate subhazard functions with complex shapes. The methods are illustrated using data from the European Blood and Marrow Transplantation Registry showing excellent agreement between parametric estimates of the cause-specific CIF and those obtained from a semi-parametric model. We also fit models relaxing the proportional subhazards assumption using alternative link functions and/or including time-dependent effects. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Competing risks occur in survival analysis when an individual is at risk of more than one type of event and one event’s occurrence precludes another’s. The cause-specific cumulative incidence function (CIF) is a measure of interest with competing-risks data. It gives the absolute (or crude) risk of having the event by time t, accounting for the fact that it is impossible to have the event if a competing event occurs first. The user-written command stcompet calculates nonparametric estimates of the cause-specific CIF, and the official Stata command stcrreg fits the Fine and Gray (1999, Journal of the American Statistical Association 94: 496–509) model for competing-risks data. Geskus (2011, Biometrics 67: 39–49) has recently shown that standard software can estimate some of the key measures in competing risks by restructuring the data and incorporating weights. This has a number of advantages because any tools developed for standard survival analysis can then be used to analyze competing-risks data. In this article, I describe the stcrprep command, which restructures the data and calculates the appropriate weights. After one uses stcrprep, a number of standard Stata survival analysis commands can then be used to analyze competing risks. For example, sts graph, failure will give a plot of the cause-specific CIF, and stcox will fit the Fine and Gray (1999) proportional subhazards model. Using stcrprep together with stcox is computationally much more efficient than using stcrreg. In addition, stcrprep opens up new opportunities for competing-risk models. I illustrate this by fitting flexible parametric survival models to the expanded data to directly model the cause-specific CIF. [ABSTRACT FROM AUTHOR]

Background: Age-standardized net survival provides an important population-based summary of cancer survival that appropriately accounts for differences in other-cause mortality rates and standardizes the population age distribution to allow fair comparisons. Recently, there has been debate over the most appropriate method for estimating this quantity, with the traditional Ederer II approach being shown to have potential bias. Methods: We compare lifetable-based estimates (Ederer II), a new unbiased method based on inverse probability of censoring weights (Pohar Perme) and model-based estimates. We make the comparison in a simulation setting; generating scenarios where we would expect to see a large theoretical bias. Results: Our simulations demonstrate that even in relatively extreme scenarios there is negligible bias in age-standardized net survival when using the age-standardized Ederer II method, modelling with continuous age or using the Pohar Perme method. However, both the Ederer II and modelling approaches have some advantages over the Pohar Perme method in terms of greater precision, particularly for longer-term follow-up (10 and 15 years). Conclusions: Our results show that, when age-standardizing, concern over bias with the traditional methods is unfounded. We have also shown advantages in using the more traditional and modelling methods. [ABSTRACT FROM AUTHOR]

Parametric survival models are being increasingly used as an alternative to the Cox model in biomedical research. Through direct modelling of the baseline hazard function, we can gain greater understanding of the risk profile of patients over time, obtaining absolute measures of risk. Commonly used parametric survival models, such as the Weibull, make restrictive assumptions of the baseline hazard function, such as monotonicity, which is often violated in clinical datasets. In this article, we extend the general framework of parametric survival models proposed by Crowther and Lambert (Journal of Statistical Software 53:12, 2013), to incorporate relative survival, and robust and cluster robust standard errors. We describe the general framework through three applications to clinical datasets, in particular, illustrating the use of restricted cubic splines, modelled on the log hazard scale, to provide a highly flexible survival modelling framework. Through the use of restricted cubic splines, we can derive the cumulative hazard function analytically beyond the boundary knots, resulting in a combined analytic/numerical approach, which substantially improves the estimation process compared with only using numerical integration. User-friendly Stata software is provided, which significantly extends parametric survival models available in standard software. [ABSTRACT FROM AUTHOR]

Parametric survival models are being increasingly used as an alternative to the Cox model in biomedical research. Through direct modelling of the baseline hazard function, we can gain greater understanding of the risk profile of patients over time, obtaining absolute measures of risk. Commonly used parametric survival models, such as the Weibull, make restrictive assumptions of the baseline hazard function, such as monotonicity, which is often violated in clinical datasets. In this article, we extend the general framework of parametric survival models proposed by Crowther and Lambert ( Journal of Statistical Software 53:12, 2013), to incorporate relative survival, and robust and cluster robust standard errors. We describe the general framework through three applications to clinical datasets, in particular, illustrating the use of restricted cubic splines, modelled on the log hazard scale, to provide a highly flexible survival modelling framework. Through the use of restricted cubic splines, we can derive the cumulative hazard function analytically beyond the boundary knots, resulting in a combined analytic/numerical approach, which substantially improves the estimation process compared with only using numerical integration. User-friendly Stata software is provided, which significantly extends parametric survival models available in standard software. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Background Methodological development of joint models of longitudinal and survival data has been rapid in recent years; however, their full potential in applied settings are yet to be fully explored. We describe a novel use of a specific association structure, linking the two component models through the subject specific intercept, and thus extend joint models to account for measurement error in a biomarker, even when only the baseline value of the biomarker is of interest. This is a common occurrence in registry data sources, where often repeated measurements exist but are simply ignored. Methods The proposed specification is evaluated through simulation and applied to data from the General Practice Research Database, investigating the association between baseline Systolic Blood Pressure (SBP) and the time-to-stroke in a cohort of obese patients with type 2 diabetes mellitus. Results By directly modelling the longitudinal component we reduce bias in the hazard ratio for the effect of baseline SBP on the time-to-stroke, showing the large potential to improve on previous prognostic models which use only observed baseline biomarker values. Conclusion The joint modelling of longitudinal and survival data is a valid approach to account for measurement error in the analysis of a repeatedly measured biomarker and a time-to-event. User friendly Stata software is provided. [ABSTRACT FROM AUTHOR]

Simulation studies are conducted to assess the performance of current and novel statistical models in pre-defined scenarios. It is often desirable that chosen simulation scenarios accurately reflect a biologically plausible underlying distribution. This is particularly important in the framework of survival analysis, where simulated distributions are chosen for both the event time and the censoring time. This paper develops methods for using complex distributions when generating survival times to assess methods in practice. We describe a general algorithm involving numerical integration and root-finding techniques to generate survival times from a variety of complex parametric distributions, incorporating any combination of time-dependent effects, time-varying covariates, delayed entry, random effects and covariates measured with error. User-friendly Stata software is provided. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Competing risks are present when the patients within a dataset could experience one or more of several exclusive events and the occurrence of any one of these could impede the event of interest. One of the measures of interest for analyses of this type is the cumulative incidence function. stpm2cif is a postestimation command used to generate predictions of the cumulative incidence function after fitting a flexible parametric survival model using stpm2. There is also the option to generate confidence intervals, cause-specific hazards, and two other measures that will be discussed in further detail. The new command is illustrated through a simple example. [ABSTRACT FROM AUTHOR]

Background: Competing risks are a common occurrence in survival analysis. They arise when a patient is at risk of more than one mutually exclusive event, such as death from different causes, and the occurrence of one of these may prevent any other event from ever happening. Methods: There are two main approaches to modelling competing risks: the first is to model the cause-specific hazards and transform these to the cumulative incidence function; the second is to model directly on a transformation of the cumulative incidence function. We focus on the first approach in this paper. This paper advocates the use of the flexible parametric survival model in this competing risk framework. Results: An illustrative example on the survival of breast cancer patients has shown that the flexible parametric proportional hazards model has almost perfect agreement with the Cox proportional hazards model. However, the large epidemiological data set used here shows clear evidence of non-proportional hazards. The flexible parametric model is able to adequately account for these through the incorporation of time-dependent effects. Conclusion: A key advantage of using this approach is that smooth estimates of both the cause-specific hazard rates and the cumulative incidence functions can be obtained. It is also relatively easy to incorporate time-dependent effects which are commonly seen in epidemiological studies. [ABSTRACT FROM AUTHOR]

Background: When interested in studying the effect of a treatment (or other exposure) on a time-to-event outcome, the most popular approach is to estimate survival probabilities using the Kaplan-Meier estimator. In the presence of confounding, regression models are fitted, and results are often summarised as hazard ratios. However, despite their broad use, hazard ratios are frequently misinterpreted as relative risks instead of relative rates.Methods: We discuss measures for summarising the analysis from a regression model that overcome some of the limitations associated with hazard ratios. Such measures are the standardised survival probabilities for treated and untreated: survival probabilities if everyone in the population received treatment and if everyone did not. The difference between treatment arms can be calculated to provide a measure for the treatment effect.Results: Using publicly available data on breast cancer, we demonstrated the usefulness of standardised survival probabilities for comparing the experience between treated and untreated after adjusting for confounding. We also showed that additional important research questions can be addressed by standardising among subgroups of the total population.Discussion: Standardised survival probabilities are a useful way to report the treatment effect while adjusting for confounding and have an informative interpretation in terms of risk. [ABSTRACT FROM AUTHOR]

Background: Relative survival is commonly used for studying survival of cancer patients as it captures both the direct and indirect contribution of a cancer diagnosis on mortality by comparing the observed survival of the patients to the expected survival in a comparable cancer-free population. However, existing methods do not allow estimation of the impact of isolated conditions (e.g., excess cardiovascular mortality) on the total excess mortality. For this purpose we extend flexible parametric survival models for relative survival, which use restricted cubic splines for the baseline cumulative excess hazard and for any time-dependent effects. Methods: In the extended model we partition the excess mortality associated with a diagnosis of cancer through estimating a separate baseline excess hazard function for the outcomes under investigation. This is done by incorporating mutually exclusive background mortality rates, stratified by the underlying causes of death reported in the Swedish population, and by introducing cause of death as a time-dependent effect in the extended model. This approach thereby enables modeling of temporal trends in e.g., excess cardiovascular mortality and remaining cancer excess mortality simultaneously. Furthermore, we illustrate how the results from the proposed model can be used to derive crude probabilities of death due to the component parts, i.e., probabilities estimated in the presence of competing causes of death. Results: The method is illustrated with examples where the total excess mortality experienced by patients diagnosed with breast cancer is partitioned into excess cardiovascular mortality and remaining cancer excess mortality. Conclusions: The proposed method can be used to simultaneously study disease patterns and temporal trends for various causes of cancer-consequent deaths. Such information should be of interest for patients and clinicians as one way of improving prognosis after cancer is through adapting treatment strategies and follow-up of patients towards reducing the excess mortality caused by side effects of the treatment. [ABSTRACT FROM AUTHOR]

Abstract: Background: Survival from breast cancer is lower in the UK than in some other European countries. We compared survival in England and Norway by age and time from diagnosis. Methods: We included 303,648 English and 24,919 Norwegian cases of breast cancer diagnosed 1996–2004 using flexible parametric relative survival models, enabling improved quantification of differences in survival. Crude probabilities were estimated to partition the probability of death due to all causes into that due to cancer and other causes and to estimate the number of “avoidable” deaths. Results: England had lower relative survival for all ages with the difference increasing with age. Much of the difference was due to higher excess mortality in England in the first few months after diagnosis. Older patients had a higher proportion of deaths due to other causes. At 5 years post diagnosis, a woman aged 85 in England had probabilities of 0.35 of dying of cancer and 0.32 of dying of other causes, whilst in Norway they were 0.26 and 0.35. By eight years the number of “avoidable” all-cause deaths in England was 1020 with the number of “avoidable” breast cancer related deaths 1488. Conclusion: Lower breast cancer survival in England is mainly due to higher mortality in the first year after diagnosis. Crude probabilities aid our understanding of the impact of disease on individual patients and help assess different treatment options. [Copyright &y& Elsevier]

Background: We investigate methods used to analyse the results of clinical trials with survival outcomes in which some patients switch from their allocated treatment to another trial treatment. These included simple methods which are commonly used in medical literature and may be subject to selection bias if patients switching are not typical of the population as a whole. Methods which attempt to adjust the estimated treatment effect, either through adjustment to the hazard ratio or via accelerated failure time models, were also considered. A simulation study was conducted to assess the performance of each method in a number of different scenarios. Results: 16 different scenarios were identified which differed by the proportion of patients switching, underlying prognosis of switchers and the size of true treatment effect. 1000 datasets were simulated for each of these and all methods applied. Selection bias was observed in simple methods when the difference in survival between switchers and non-switchers were large. A number of methods, particularly the AFT method of Branson and Whitehead were found to give less biased estimates of the true treatment effect in these situations. Conclusions: Simple methods are often not appropriate to deal with treatment switching. Alternative approaches such as the Branson & Whitehead method to adjust for switching should be considered. [ABSTRACT FROM AUTHOR]

Large age-dependant differences in temporal trends in 1- and 5-year relative survival have been observed in patients with acute myeloid leukaemia (AML) in Sweden. This investigation used an alternative approach to studying patient survival that simultaneously estimated the proportion of patients cured from their cancer and the survival of the ‘uncured’. We conducted a population-based study including 6439 AML patients aged 19–80 years in Sweden between 1973 and 2001. Mixture cure models were estimated, with age at diagnosis categorised (19–40, 41–60, 61–70 and 71–80) and year of diagnosis modelled using splines. In 1975 the cure proportion was ≤6% in all age groups and the median survival time for ‘uncured’ patients was <0·5 years. In 2000 the cure proportion was 68% (95% confidence interval 56–77%) in the youngest group, and 32% (25–39%), 8% (3–21%), and 4% (2–8%) in the other groups, respectively. The median survival times for ‘uncured’ were 0·74 (0·43–1·26), 0·71 (0·53–0·97), 0·69 (0·51–0·95) and 0·37 (0·31–0·44) years, respectively. A dramatic improvement in the cure proportion was seen in younger patients, whereas improvement in older ages was mainly within the survival of the ‘uncured’. This novel approach of analysing survival data could be a valuable tool for physicians, patients, health care planners and health economists. [ABSTRACT FROM AUTHOR]

The article explores the rationale for the use of individual participant data instead of aggregate data in meta-analyses and outlines how to conduct such a type of study. It discusses the meaning of the term "individual participant data" and explains an individual participant data meta-analysis. It examines the incidence of individual participant data meta-analysis over time and the correlation between aggregate data meta-analysis and an individual participant data meta-analysis. The advantages of meta-analysis of individual participant data are also cited.

Objective To compare four potential screening strategies, and subsequent interventions, for the prevention and treatment of type 2 diabetes: (a) screening for type 2 diabetes to enable early detection and treatment, (b) screening for type 2 diabetes and impaired glucose tolerance, interveningwith lifestyle interventions in those with a diagnosis of impaired glucose tolerance to delay or prevent diabetes, (c) as for (b) but with pharmacological interventions, and (d) no screening. Design Cost effectiveness analysis based on development and evaluation of probabilistic, comprehensive economic decision analytic model, from screening to death. Setting A hypothetical population, aged 45 at time of screening, with above average-risk of diabetes. Data sources Published clinical trials and epidemiological studies retrieved from electronic bibliographic databases; supplementary data obtained from the Department of Health statistics for England and Wales, the screening those at risk (STAR) study, and the Leicester division of the ADDITION study. Methods A hybrid decision tree/Markov model was developed to simulate the long term effects of each screening strategy, in terms of both clinical and cost effectiveness outcomes. The base case model assumed a 50 year time horizon with discounting of both costs and benefits at 3.5%. Sensitivity analyses were carried out to investigate assumptions of the model and to identify which model inputs had most impact on the results. Results Estimated costs for each quality adjusted life year (QALY) gained (discounted at 3.5% a year for both costs and benefits) were £14 150 (¿17 560; $27 860>)for screening for type 2 diabetes, £6242 for screening for diabetes and impaired glucose tolerance followed by lifestyle interventions, and £7023 for screening for diabetes and impaired glucose tolerance followed by pharmacological interventions, all compared with no screening. At a willingness-to-pay threshold of £20 000 the probability of the intervention being cost effective was 49%, 93%, and 85% for each of the active screening strategies respectively. Conclusions Screening for type 2 diabetes and impaired glucose tolerance, with appropriate intervention for those with impaired glucose tolerance, in an above average risk population aged 45, seems to be cost effective. The cost effectiveness of a policy of screening for diabetes alone, which offered no intervention to those with impaired glucose tolerance, is still uncertain, and further research on the impact of early detection of diabetes is needed. [ABSTRACT FROM AUTHOR]

Meta-analysis of individual patient data (IPD) is the gold-standard for synthesizing evidence across clinical studies. However, for some studies IPD may not be available and only aggregate data (AD), such as a treatment effect estimate and its standard error, may be obtained. In this situation, methods for combining IPD and AD are important to utilize all the available evidence. In this paper, we develop and assess a range of statistical methods for combining IPD and AD in meta-analysis of continuous outcomes from randomized controlled trials. The methods take either a one-step or a two-step approach. The latter is simple, with IPD reduced to AD so that standard AD meta-analysis techniques can be employed. The one-step approach is more complex but offers a flexible framework to include both patient-level and trial-level parameters. It uses a dummy variable to distinguish IPD trials from AD trials and to constrain which parameters the AD trials estimate. We show that this is important when assessing how patient-level covariates modify treatment effect, as aggregate-level relationships across trials are subject to ecological bias and confounding. We thus develop models to separate within-trial and across-trials treatment-covariate interactions; this ensures that only IPD trials estimate the former, whilst both IPD and AD trials estimate the latter in addition to the pooled treatment effect and any between-study heterogeneity. Extension to multiple correlated outcomes is also considered. Ten IPD trials in hypertension, with blood pressure the continuous outcome of interest, are used to assess the models and identify the benefits of utilizing AD alongside IPD. Copyright © 2007 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Relative survival is frequently used in population-based studies as a method for estimating disease-related mortality without the need for information on cause of death. We propose an extension to relative survival of a flexible parametric model proposed by Royston and Parmar for censored survival data. The model provides smooth estimates of the relative survival and excess mortality rates by using restricted cubic splines on the log cumulative excess hazard scale. The approach has several advantages over some of the more standard relative survival models, which adopt a piecewise approach, the main being the ability to model time on a continuous scale, the survival and hazard functions are obtained analytically and it does not use split-time data. Copyright © 2007 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Relative survival is used to estimate patient survival excluding causes of death not related to the disease of interest. Rather than using cause of death information from death certificates, which is often poorly recorded, relative survival compares the observed survival to that expected in a matched group from the general population. Models for relative survival can be expressed on the hazard (mortality) rate scale as the sum of two components where the total mortality rate is the sum of the underlying baseline mortality rate and the excess mortality rate due to the disease of interest. Previous models for relative survival have assumed that covariate effects act multiplicatively and have thus provided relative effects of differences between groups using excess mortality rate ratios. In this paper we consider (i) the use of an additive covariate model, which provides estimates of the absolute difference in the excess mortality rate; and (ii) the use of fractional polynomials in relative survival models for the baseline excess mortality rate and time-dependent effects. The approaches are illustrated using data on 115 331 female breast cancer patients diagnosed between 1 January 1986 and 31 December 1990. The use of additive covariate relative survival models can be useful in situations when the excess mortality rate is zero or slightly less than zero and can provide useful information from a public health perspective. The use of fractional polynomials has advantages over the usual piecewise estimation by providing smooth estimates of the baseline excess mortality rate and time-dependent effects for both the multiplicative and additive covariate models. All models presented in this paper can be estimated within a generalized linear models framework and thus can be implemented using standard software. Copyright © 2005 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

There has been a recent growth in the use of Bayesian methods in medical research. The main reasons for this are the development of computer intensive simulation based methods such as Markov chain Monte Carlo (MCMC), increases in computing power and the introduction of powerful software such as WinBUGS. This has enabled increasingly complex models to be fitted. The ability to fit these complex models has led to MCMC methods being used as a convenient tool by frequentists, who may have no desire to be fully Bayesian. Often researchers want 'the data to dominate' when there is no prior information and thus attempt to use vague prior distributions. However, with small amounts of data the use of vague priors can be problematic. The results are potentially sensitive to the choice of prior distribution. In general there are fewer problems with location parameters. The main problem is with scale parameters. With scale parameters, not only does one have to decide the distributional form of the prior distribution, but also whether to put the prior distribution on the variance, standard deviation or precision. We have conducted a simulation study comparing the effects of 13 different prior distributions for the scale parameter on simulated random effects meta-analysis data. We varied the number of studies (5, 10 and 30) and compared three different between-study variances to give nine different simulation scenarios. One thousand data sets were generated for each scenario and each data set was analysed using the 13 different prior distributions. The frequentist properties of bias and coverage were investigated for the between-study variance and the effect size. The choice of prior distribution was crucial when there were just five studies. There was a large variation in the estimates of the between-study variance for the 13 different prior distributions. With a large number of studies the choice of prior distribution was less important. The effect size estimated was not biased, but the precision with which it was estimated varied with the choice of prior distribution leading to varying coverage intervals and, potentially, to different statistical inferences. Again there was less of a problem with a larger number of studies. There is a particular problem if the between-study variance is close to the boundary at zero, as MCMC results tend to produce upwardly biased estimates of the between-study variance, particularly if inferences are based on the posterior mean. The choice of 'vague' prior distribution can lead to a marked variation in results, particularly in small studies. Sensitivity to the choice of prior distribution should always be assessed. Copyright © 2005 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Peak expiratory flow (PEF) is a measure commonly used in clinical practice and research for respiratory diseases such as asthma. In research, PEF is usually recorded in a diary for a 2-week period with two or more measurements per day. Interest may lie in whether certain groups of individuals tend to have higher or lower PEF. In addition the variability of PEF may be of interest as, for example, asthmatics tend to have more variable airways. In this paper we develop a three-level hierarchical model that can simultaneously model the mean level and variability of PEF. The variability is broken down into three components, between-subject variability, between-day within-subject variability, and within-day within-subject variability. The latter two components are of specific clinical interest. We fit both classical and Bayesian models. The Bayesian models have the advantage of taking the uncertainty in the variance component estimates into account when estimating the standard errors of the fixed effects. In addition, the Bayesian models provide an intuitive and simple way to investigate the within-subject variance components. Copyright © 2004 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

: ObjectiveStandard survival methods can yield out-of-date estimates of long-term survival. Period analysis, based on life-table methodology, provides more up-to-date survival estimates by exploring survival during a restricted recent period of interest. It excludes the short-term survival of patients recruited at the start of the study. We use statistical models to further develop the method of period analysis, providing more up-to-date estimates of survival and the ability to explore differences in survival by covariates and adjust for case mix.: MethodsWe use cancer registry data for colorectal cancer in Leicestershire, UK, to illustrate the use of Cox proportional hazards (CPH) models to estimate period and standard survival. We compare these estimates with those obtained using life-table methodology.: ResultsPeriod estimates were slightly higher than the standard estimates as they reflect recent improvements in short-term survival. The results for period analysis using the life-table approach and using CPH models were similar. However, CPH models allowed further investigation of other risk factors and the ability to control for potential confounding variables.: ConclusionUsing period survival estimates, more up-to-date information is available to clinicians and others with an interest in monitoring survival. Period CHP models offer all the advantages of statistical modeling, and are straightforward to fit in standard statistical packages. [Copyright &y& Elsevier]

Background Episodic wheeze triggered by viral colds is common in children aged between 1 and 5 years (preschool viral wheeze). Most affected children are asymptomatic by age 6 years. Persistence of wheeze is associated with above average systemic eosinophil priming. Use of parentalinitiated oral prednisolone is recommended at the first sign of preschool viral wheeze. However, evidence for this treatment strategy is conflicting. We therefore aimed to assess the efficacy of a short course of oral prednisolone for preschool viral wheeze, with stratification for systemic eosinophil priming. Methods Children aged 1--5 years admitted to hospital with viral wheeze were allocated to either a high-primed or lowprimed stratum according to amounts of serum eosinophil cationic protein and eosinophil protein X, and randomised to parent-initiated prednisolone (20 mg one daily for 5 days) or placebo for the next episode. The primary outcomes were the 7-day mean daytime and night-time respiratory symptom scores, which were analysed by mean differences between treatment groups. Findings 108 children were randomised to placebo and 109 to prednisolone. Outcome data were available for 120 (78%) of 153 children who had a further episode of viral wheeze, of whom 51 received prednisolone and 69 placebo. Mean daytime (difference in means --0.01 [--0.22 to 0.20]) and night-time (0.10 [--0.12 to 0.32]) respiratory symptom scores and need for hospital admission did not differ between treatment groups. Within the high-primed (n=59) and lowprimed (n=61) strata there was no difference in primary outcome between treatment groups. Interpretation There is no clear benefit of a short course of parent-initiated oral prednisolone for viral wheeze in children aged 1--5 years even in those with above-average eosinophil priming. [ABSTRACT FROM AUTHOR]

Background: Multi-state models are being increasingly used to capture complex disease pathways. The convenient formula of the exponential multi-state model can facilitate a quick and accessible understanding of the data. However, assuming time constant transition rates is not always plausible. On the other hand, obtaining predictions from a fitted model with time-dependent transitions can be challenging. One proposed solution is to utilise a general simulation algorithm to calculate predictions from a fitted multi-state model.Methods: Predictions obtained from an exponential multi-state model were compared to those obtained from two different parametric models and to non-parametric Aalen-Johansen estimates. The first comparative approach fitted a multi-state model with transition-specific distributions, chosen separately based on the Akaike Information Criterion. The second approach was a Royston-Parmar multi-state model with 4 degrees of freedom, which was chosen as a reference model flexible enough to capture complex hazard shapes. All quantities were obtained analytically for the exponential and Aalen-Johansen approaches. The transition rates for the two comparative approaches were also obtained analytically, while all other quantities were obtained from the fitted models via a general simulation algorithm. Metrics investigated were: transition probabilities, attributable mortality (AM), population attributable fraction (PAF) and expected length of stay. This work was performed on previously analysed hospital acquired infection (HAI) data. By definition, a HAI takes three days to develop and therefore selected metrics were also predicted from time 3 (delayed entry).Results: Despite clear deviations from the constant transition rates assumption, the empirical estimates of the transition probabilities were approximated reasonably well by the exponential model. However, functions of the transition probabilities, e.g. AM and PAF, were not well approximated and the comparative models offered considerable improvements for these metrics. They also provided consistent predictions with the empirical estimates in the case of delayed entry time, unlike the exponential model.Conclusion: We conclude that methods and software are readily available for obtaining predictions from multi-state models that do not assume constant transition rates. The multistate package in Stata facilitates a range of predictions with confidence intervals, which can provide a more comprehensive understanding of the data. User-friendly code is provided. [ABSTRACT FROM AUTHOR]

Abstract Objective: To assess the effect of an NHS walk-in centre on local primary and emergency healthcare services. Design: Before and after observational study. Setting: Loughborough, which had an NHS walk-in centre, and Market Harborough, the control town. Participants: 12 general practices. Main outcome measures: Mean daily rate of emergency general practitioner consultations, mean number of haft days to the sixth bookable routine appointment, and attendance rates at out of hours services, minor injuries units, and accident and emergency departments. Results: The change between the before and after study periods was not significantly different in the two towns for daily rate of emergency general practice consultations (mean difference - 0.02/1000 population, 95% confidence interval - 0.75 to 0.71), the time to the sixth bookable routine appointment ( -0.24 half-days, -1.85 to 1.37), and daily rate of attendances at out of hours services (0.07/1000 population, -0.06 to 0.19). However, attendance at the local minor injuries unit was significantly higher in Loughborough than Market Harborough (rate ratio 1.22, 1.12 to 1.33). Non-ambulance attendances at accident and emergency departments fell less in Loughborough than Market Harborough (rate ratio 1.17, 1.03 to 1.33). Conclusions: The NHS walk-in centre did not greatly affect the workload of local general practitioners. However, the workload of the local minor injuries unit increased significantly, probably because it was in the same building as the walk-in centre. [ABSTRACT FROM AUTHOR]

A commonly reported measure when interested in the survival of cancer patients is relative survival. Relative survival circumvents issues with inaccurate cause of death information by incorporating the expected mortality rates of cancer individuals from population lifetables of the general population. A summary of the cancer population prognosis can be obtained using the marginal relative survival. To explore differences between exposure groups, such as socioeconomic groups, the difference in marginal relative survival between exposed and unexposed can be obtained and under assumptions is interpreted as the average causal effect of exposure to survival. In a modeling context, this is usually estimated by applying regression standardization as the average of the individual‐specific estimates after fitting a relative survival model. Regression standardization yields an estimator that consistently estimates the causal effect under standard causal inference assumptions and if the relative survival model is correctly specified. We extend inverse probability weighting (IPW) and doubly robust standardization methods in the relative survival framework as additional valuable tools for obtaining average causal effects when correct model specification might not hold for the relative survival model. IPW yields an unbiased estimate of the average causal effect if a correctly specified model has been fitted for the exposure (propensity score) whereas doubly robust standardization requires that at least one of the propensity score model or the relative survival model is correctly specified. An example using data on melanoma is provided and a simulation study is conducted to investigate how sensitive are the methods to model misspecification, including different ways for obtaining standard errors. [ABSTRACT FROM AUTHOR]

A long-term, constant, protective treatment effect is a strong assumption when extrapolating survival beyond clinical trial follow-up; hence, sensitivity to treatment effect waning is commonly assessed for economic evaluations. Forcing a hazard ratio (HR) to 1 does not necessarily estimate loss of individual-level treatment effect accurately because of HR selection bias. A simulation study was designed to explore the behavior of marginal HRs under a waning conditional (individual-level) treatment effect and demonstrate bias in forcing a marginal HR to 1 when the estimand is "survival difference with individual-level waning". Data were simulated under 4 parameter combinations (varying prognostic strength of heterogeneity and treatment effect). Time-varying marginal HRs were estimated in scenarios where the true conditional HR attenuated to 1. Restricted mean survival time differences, estimated having constrained the marginal HR to 1, were compared with true values to assess bias induced by marginal constraints. Under loss of conditional treatment effect, the marginal HR took a value >1 because of covariate imbalances. Constraining this value to 1 lead to restricted mean survival time difference bias of up to 0.8 years (57% increase). Inflation of effect size estimates also increased with the magnitude of initial protective treatment effect. Important differences exist between survival extrapolations assuming marginal versus conditional treatment effect waning. When a marginal HR is constrained to 1 to assess efficacy under individual-level treatment effect waning, the survival benefits associated with the new treatment will be overestimated, and incremental cost-effectiveness ratios will be underestimated. • Noncollapsibility of odds/hazard ratios (HRs) has been widely discussed in causal inference literature. Discussion of the impact in randomized controlled trial extrapolations is scarcer. This article explores the importance of understanding the difference between marginal and conditional HRs, specifically in the context of an economic evaluation/health technology assessment using randomized controlled trial data, carrying out an extrapolation assuming treatment effect waning. • If the individual-level treatment effect truly wanes after a period of protective treatment effect, a marginal (eg, unadjusted) HR > 1 from this point is to be expected. This reflects covariate imbalances between arms induced by the treatment's effect and demonstrates the difficulty in interpreting a HR that is dependent on both participant-level hazards and trial-level covariate distributions (and how these shift over time). • Forcing a marginal HR to 1 from the end of trial follow-up is not equivalent to individual-level treatment effect loss (inducing bias in restricted mean survival time difference of up to 0.8 years, increasing with treatment strength and prognostic power of marginal covariates). To most accurately assess sensitivity to treatment effect waning, constraints on the HR should be applied to an estimate that is conditional on all feasible prognostic factors. [ABSTRACT FROM AUTHOR]

Background: The occurrence of a range of health outcomes following myocardial infarction (MI) is unknown. Therefore, this study aimed to determine the long-term risk of major health outcomes following MI and generate sociodemographic stratified risk charts in order to inform care recommendations in the post-MI period and underpin shared decision making. Methods and findings: This nationwide cohort study includes all individuals aged ≥18 years admitted to one of 229 National Health Service (NHS) Trusts in England between 1 January 2008 and 31 January 2017 (final follow-up 27 March 2017). We analysed 11 non-fatal health outcomes (subsequent MI and first hospitalisation for heart failure, atrial fibrillation, cerebrovascular disease, peripheral arterial disease, severe bleeding, renal failure, diabetes mellitus, dementia, depression, and cancer) and all-cause mortality. Of the 55,619,430 population of England, 34,116,257 individuals contributing to 145,912,852 hospitalisations were included (mean age 41.7 years (standard deviation [SD 26.1]); n = 14,747,198 (44.2%) male). There were 433,361 individuals with MI (mean age 67.4 years [SD 14.4)]; n = 283,742 (65.5%) male). Following MI, all-cause mortality was the most frequent event (adjusted cumulative incidence at 9 years 37.8% (95% confidence interval [CI] [37.6,37.9]), followed by heart failure (29.6%; 95% CI [29.4,29.7]), renal failure (27.2%; 95% CI [27.0,27.4]), atrial fibrillation (22.3%; 95% CI [22.2,22.5]), severe bleeding (19.0%; 95% CI [18.8,19.1]), diabetes (17.0%; 95% CI [16.9,17.1]), cancer (13.5%; 95% CI [13.3,13.6]), cerebrovascular disease (12.5%; 95% CI [12.4,12.7]), depression (8.9%; 95% CI [8.7,9.0]), dementia (7.8%; 95% CI [7.7,7.9]), subsequent MI (7.1%; 95% CI [7.0,7.2]), and peripheral arterial disease (6.5%; 95% CI [6.4,6.6]). Compared with a risk-set matched population of 2,001,310 individuals, first hospitalisation of all non-fatal health outcomes were increased after MI, except for dementia (adjusted hazard ratio [aHR] 1.01; 95% CI [0.99,1.02];p = 0.468) and cancer (aHR 0.56; 95% CI [0.56,0.57];p < 0.001). The study includes data from secondary care only—as such diagnoses made outside of secondary care may have been missed leading to the potential underestimation of the total burden of disease following MI. Conclusions: In this study, up to a third of patients with MI developed heart failure or renal failure, 7% had another MI, and 38% died within 9 years (compared with 35% deaths among matched individuals). The incidence of all health outcomes, except dementia and cancer, was higher than expected during the normal life course without MI following adjustment for age, sex, year, and socioeconomic deprivation. Efforts targeted to prevent or limit the accrual of chronic, multisystem disease states following MI are needed and should be guided by the demographic-specific risk charts derived in this study. Using hospital records of 34 million adults admitted to hospitals in England, Marlous Hall and team examine the long-term risk of major health outcomes following myocardial infarction. Author summary: Why was this study done?: Myocardial infarction (MI; heart attack) can have major long-term impact on individuals and result in a wide range of further health conditions. Existing studies have focussed on determining the short-term risk of a second heart attack, stroke, or major bleeding, but research describing the long-term risk of major health outcomes for specific age, sex, and deprivation groups was lacking. Nationally representative and robust information of a wide range of long-term health outcomes following a heart attack is critical for the development of treatment recommendations, which take account of an individuals' specific risk. What did the researchers do and find?: From the population of 56 million adults in England, we analysed hospital records for 34 million adults admitted to hospital (constituting 145 million admission records) to investigate the long-term health outcomes following a heart attack compared with individuals without a heart attack. Of 433,361 individuals with a heart attack, up to a third developed heart failure or renal failure, 7% had further heart attacks, and 38% died within the 9-year study period. Heart failure, atrial fibrillation, stroke, peripheral arterial disease, severe bleeding, renal failure, diabetes, and depression occurred more frequently for people who had a heart attack compared with those who did not, but the risk of cancer was lower overall and the risk of dementia did not differ overall. What do these findings mean?: Efforts should be made to prevent or limit the development of long-term health outcomes that follow a heart attack—the likelihood of which differ depending on the age, sex, and deprivation of an individual. These findings are based on the full population of adults admitted to hospital in England, address limitations of previous studies, and can be used to inform preventative strategies tailored to specific individuals surviving a heart attack. The study was limited to hospitalisation data only—therefore, some diagnoses made outside of hospital may have been missed. [ABSTRACT FROM AUTHOR]

When analyzing cohort data, we rarely consider potential simultaneous effects of multiple time-scales on estimated survival proportions and hazard ratios. Instead, often, only one time-scale, such as time-on-study or attained age, is taken as the main time-scale and the other time-scale(s) included in the model as time-fixed covariate(s). In this simulation study, we investigate the potential bias in estimated survival proportions and hazard ratios by not modeling multiple time-scales. We simulate data with two time-scales, under various scenarios, and a binary covariate as the exposure of interest. Flexible parametric models with one time-scale were fitted to the simulated data, including the exposure variable of interest and the other time-scale as a fixed covariate. For many of our simulated scenarios, the models with one time-scale showed small bias for the survival proportions. Bias in the log hazard ratios (for the covariate of interest) was observed when there were non-proportional hazards on the second time-scale. If one is modeling only one time-scale it is advisable to include non-proportional hazards and interactions with the time-scale. Exploration of approaches to modeling multiple time-scales is outside the scope of this study. [ABSTRACT FROM AUTHOR]

Background: Royston-Parmar flexible parametric survival models (FPMs) can be fitted on either the cause-specific hazards or cumulative incidence scale in the presence of competing risks. An advantage of modelling within this framework for competing risks data is the ease at which alternative predictions to the (cause-specific or subdistribution) hazard ratio can be obtained. Restricted mean survival time (RMST), or restricted mean failure time (RMFT) on the mortality scale, is one such measure. This has an attractive interpretation, especially when the proportionality assumption is violated. Compared to similar measures, fewer assumptions are required and it does not require extrapolation. Furthermore, one can easily obtain the expected number of life-years lost, or gained, due to a particular cause of death, which is a further useful prognostic measure as introduced by Andersen.Methods: In the presence of competing risks, prediction of RMFT and the expected life-years lost due to a cause of death are presented using Royston-Parmar FPMs. These can be predicted for a specific covariate pattern to facilitate interpretation in observational studies at the individual level, or at the population-level using standardisation to obtain marginal measures. Predictions are illustrated using English colorectal data and are obtained using the Stata post-estimation command, standsurv.Results: Reporting such measures facilitate interpretation of a competing risks analysis, particularly when the proportional hazards assumption is not appropriate. Standardisation provides a useful way to obtain marginal estimates to make absolute comparisons between two covariate groups. Predictions can be made at various time-points and presented visually for each cause of death to better understand the overall impact of different covariate groups.Conclusions: We describe estimation of RMFT, and expected life-years lost partitioned by each competing cause of death after fitting a single FPM on either the log-cumulative subdistribution, or cause-specific hazards scale. These can be used to facilitate interpretation of a competing risks analysis when the proportionality assumption is in doubt. [ABSTRACT FROM AUTHOR]

Purpose: This study introduces a novel method for estimating the variance of life expectancy since diagnosis (LEC) and loss in life expectancy (LLE) for cancer patients within a relative survival framework in situations where life tables based on the entire general population are not accessible. LEC and LLE are useful summary measures of survival in population-based cancer studies, but require information on the mortality in the general population. Our method addresses the challenge of incorporating the uncertainty of expected mortality rates when using a sample from the general population. Methods: To illustrate the approach, we estimated LEC and LLE for patients diagnosed with colon and breast cancer in Sweden. General population mortality rates were based on a random sample drawn from comparators of a matched cohort. Flexible parametric survival models were used to model the mortality among cancer patients and the mortality in the random sample from the general population. Based on the models, LEC and LLE together with their variances were estimated. The results were compared with those obtained using fixed expected mortality rates. Results: By accounting for the uncertainty of expected mortality rates, the proposed method ensures more accurate estimates of variances and, therefore, confidence intervals of LEC and LLE for cancer patients. This is particularly valuable for older patients and some cancer types, where underestimation of the variance can be substantial when the entire general population data are not accessible. Conclusion: The method can be implemented using existing software, making it accessible for use in various cancer studies. The provided example of Stata code further facilitates its adoption. [ABSTRACT FROM AUTHOR]

We have previously proposed temporal recalibration to account for trends in survival over time to improve the calibration of predictions from prognostic models for new patients. This involves first estimating the predictor effects using data from all individuals (full dataset) and then re‐estimating the baseline using a subset of the most recent data whilst constraining the predictor effects to remain the same. In this article, we demonstrate how temporal recalibration can be applied in competing risk settings by recalibrating each cause‐specific (or subdistribution) hazard model separately. We illustrate this using an example of colon cancer survival with data from the Surveillance Epidemiology and End Results (SEER) program. Data from patients diagnosed in 1995–2004 were used to fit two models for deaths due to colon cancer and other causes respectively. We discuss considerations that need to be made in order to apply temporal recalibration such as the choice of data used in the recalibration step. We also demonstrate how to assess the calibration of these models in new data for patients diagnosed subsequently in 2005. Comparison was made to a standard analysis (when improvements over time are not taken into account) and a period analysis which is similar to temporal recalibration but differs in the data used to estimate the predictor effects. The 10‐year calibration plots demonstrated that using the standard approach over‐estimated the risk of death due to colon cancer and the total risk of death and that calibration was improved using temporal recalibration or period analysis. [ABSTRACT FROM AUTHOR]

Background: The impact of cardiovascular disease (CVD) comorbidity on resection rates and survival for patients with early-stage non-small-cell lung cancer (NSCLC) is unclear. We explored if CVD comorbidity explained surgical resection rate variation and the impact on survival if resection rates increased.Methods: Cancer registry data consisted of English patients diagnosed with NSCLC from 2012 to 2016. Linked hospital records identified CVD comorbidities. We investigated resection rate variation by geographical region using funnel plots; resection and death rates using time-to-event analysis. We modelled an increased propensity for resection in regions with the lowest resection rates and estimated survival change.Results: Among 57,373 patients with Stage 1-3A NSCLC, resection rates varied considerably between regions. Patients with CVD comorbidity had lower resection rates and higher mortality rates. CVD comorbidity explained only 1.9% of the variation in resection rates. For every 100 CVD comorbid patients, increasing resection in regions with the lowest rates from 24 to 44% would result in 16 more patients resected and alive after 1 year and two fewer deaths overall.Conclusions: Variation in regional resection rate is not explained by CVD comorbidities. Increasing resection in patients with CVD comorbidity to the levels of the highest resecting region would increase 1-year survival. [ABSTRACT FROM AUTHOR]

Background: In population-based cancer survival studies, the event of interest is usually death due to cancer. However, other competing events may be present. Relative survival is a commonly used measure in cancer studies that circumvents problems caused by the inaccuracy of the cause of death information. A summary of the prognosis of the cancer population and potential differences between subgroups can be obtained using marginal estimates of relative survival.Methods: We utilize regression standardization to obtain marginal estimates of interest in a relative survival framework. Such measures include the standardized relative survival, standardized all-cause survival and standardized crude probabilities of death. Contrasts of these can be formed to explore differences between exposure groups and under certain assumptions are interpreted as causal effects. The difference in standardized all-cause survival can also provide an estimate for the impact of eliminating cancer-related differences between exposure groups. The potential avoidable deaths after such hypothetical scenarios can also be estimated. To illustrate the methods we use the example of survival differences across socio-economic groups for colon cancer.Results: Using relative survival, a range of marginal measures and contrasts were estimated. For these measures we either focused on cancer-related differences only or chose to incorporate both cancer and other cause differences. The impact of eliminating differences between groups was also estimated. Another useful way for quantifying that impact is the avoidable deaths under hypothetical scenarios.Conclusions: Marginal estimates within the relative survival framework provide useful summary measures and can be applied to better understand differences across exposure groups. [ABSTRACT FROM AUTHOR]

Background: Routine reporting of cancer patient survival is important, both to monitor the effectiveness of health care and to inform about prognosis following a cancer diagnosis. A range of different survival measures exist, each serving different purposes and targeting different audiences. It is important that routine publications expand on current practice and provide estimates on a wider range of survival measures. We examine the feasibility of automated production of such statistics. Methods: We used data on 23 cancer sites obtained from the Cancer Registry of Norway (CRN). We propose an automated way of estimating flexible parametric relative survival models and calculating estimates of net survival, crude probabilities, and loss in life expectancy across many cancer sites and subgroups of patients. Results: For 21 of 23 cancer sites, we were able to estimate survival models without assuming proportional hazards. Reliable estimates of all desired measures were obtained for all cancer sites. Discussion: It may be challenging to implement new survival measures in routine publications as it can require the application of modeling techniques. We propose a way of automating the production of such statistics and show that we can obtain reliable estimates across a range of measures and subgroups of patients. [ABSTRACT FROM AUTHOR]

Background: Age-standardized net survival provides an important population-based summary of cancer survival that appropriately accounts for differences in other-cause mortality rates and standardizes the population age distribution to allow fair comparisons. Recently, there has been debate over the most appropriate method for estimating this quantity, with the traditional Ederer II approach being shown to have potential bias.Methods: We compare lifetable-based estimates (Ederer II), a new unbiased method based on inverse probability of censoring weights (Pohar Perme) and model-based estimates. We make the comparison in a simulation setting; generating scenarios where we would expect to see a large theoretical bias.Results: Our simulations demonstrate that even in relatively extreme scenarios there is negligible bias in age-standardized net survival when using the age-standardized Ederer II method, modelling with continuous age or using the Pohar Perme method. However, both the Ederer II and modelling approaches have some advantages over the Pohar Perme method in terms of greater precision, particularly for longer-term follow-up (10 and 15 years).Conclusions: Our results show that, when age-standardizing, concern over bias with the traditional methods is unfounded. We have also shown advantages in using the more traditional and modelling methods. [ABSTRACT FROM AUTHOR]